AI Engineer, AI & Applications

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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About this role

AI Engineer, AI & Applications Singapore or Australia Role Summary The AI Engineer will establish Firmus AI Factory as the foundation for efficient, production-grade distributed training by delivering pre-built training recipes (TorchTitan, Megatron etc.), evaluation benchmarks, and model guidance. You'll work with customers and internal teams to optimize training efficiency, define baselines, and document best practices. Your templates and benchmarks are the anchor point for our hyperscale customers' training workflows and our model arena differentiator. Key Responsibilities - Build production-ready training recipes using TorchTitan and Megatron-LM: model configs, parallelism strategies (FSDP, tensor/pipeline parallelism), checkpointing patterns. - Document parameter tuning for different scales (e.g., "to train Llama 7B on 8xH100s, use this config and expect X throughput"). - Create and validate multi-node NCCL communication patterns on AI Factory K8s/Slurm clusters. - Design and build benchmarking suites: accuracy, latency, throughput (tokens/sec), cost per token, energy efficiency, MFU. - Implement offline evaluation harnesses for standardized model comparison and leaderboard tracking. - Conduct fine-tuning experiments (LoRA, QLoRA) where they improve product outcomes (e.g., ops domain data), document gains. - Create training efficiency playbooks and publish benchmark results so customers can optimize workloads. - Partner with job scheduling and orchestration engineers on template integration and other AI engineers and software engineers on model optimization trade-offs for inferencing and AI applications. Skills & Experience - 5-7 years of experience in distributed machine learning (PyTorch/JAX, FSDP, DeepSpeed, multi-node training at 10+ GPUs). - Expert-level understanding of GPU optimization: utilization, memory patterns, communication bottlenecks (NCCL collectives). - Hands-on

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